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Robust Optimization of Variable-Camber Continuous Trailing-Edge Flap System Action using Stochastic Kriging

机译:使用随机Kriging的可变弯度连续后缘襟翼系统动作的鲁棒优化

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A robust optimization method is described in this paper, focused on the optimization of a Variable-Camber Continuous Trailing-Edge Flap (VCCTEF) system for drag reduction in transonic cruise. The VCCTEF, through the rich combination of shapes it can attain, has the potential to provide greater design and operation freedom than conventional flap systems and greater efficiency in drag reduction and performance improvement. The VCCTEF deformation shape optimization, however, when based on rigorous CFD simulations, presents a significant computational challenge because of the high cost of repetitive analyses required. The construction of surrogate models and their utilization in the optimization process is one common way for tackling this challenge. Here Stochastic Kriging (SK) is used, extending the commonly used Deterministic Kriging (DK). Accounting for the intrinsic uncertainty of data, Stochastic Kriging and the optimization based on it are especially suited for addressing uncertain flight conditions as well as math modeling limitations. An optimization engine is presented that couples geometric shape synthesis with CFD simulations and an automated optimization process focused on the VCCTEF system. A potential for drag reduction is shown even in the presence of uncertainties.
机译:本文描述了一种鲁棒的优化方法,重点是优化跨腔连续后缘襟翼(VCCTEF)系统,以减少跨音速巡航中的阻力。与传统的襟翼系统相比,VCCTEF通过其丰富的形状组合,具有提供更大设计和操作自由度的潜力,并且在减少阻力和提高性能方面具有更高的效率。但是,基于严格的CFD模拟时,VCCTEF变形形状优化会带来巨大的计算挑战,因为需要进行重复分析的成本很高。替代模型的构建及其在优化过程中的利用是应对这一挑战的一种常用方法。这里使用随机克里格(SK),扩展了常用的确定性克里格(DK)。考虑到数据的固有不确定性,随机克里金法和基于数据的优化特别适合解决不确定的飞行条件以及数学建模的局限性。提出了一种优化引擎,该引擎将几何形状合成与CFD模拟和专注于VCCTEF系统的自动优化过程结合在一起。即使存在不确定性,也显示出降低阻力的潜力。

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